‘I Don't Know Why I Feel So Bad Being Asian’: A Qualitative Inquiry of Anti‐Asian Racism From a Racial Trauma Perspective
Bibliographic record
Abstract
ABSTRACT Despite the incorporation of multiculturalism into Canadian federal policies since the 1970s, whiteness continues to dominate societal norms, perpetuating the racialisation of people of colour. Racialised adolescents are particularly vulnerable to the harmful effects of racialisation and racism. Focusing on Asian Canadian youth, this study adopts a racial trauma perspective to explore their experiences growing up in Canada and the impacts of racism. A total of 36 Asian Canadian youth (aged 14–23) participated in a focus group. Data were analysed using reflexive thematic analysis. Participants reported experiences of alienation and being ‘othered’ during their upbringing. Anti‐Asian racism in Canada often appears in subtle, unacknowledged forms, affecting youth from an early age. These experiences erode self‐esteem and identity, leading some to internalise them as normal and inevitable. Some Asian youth suppress these experiences, gaslighting themselves into self‐blame or denying their existence altogether. Others cope by conforming to whiteness, erasing aspects of their Asian identities. This study highlights the ways in which racial trauma manifests among Asian Canadian youth growing up in a society deeply entrenched in a white racial order, as well as its enduring impacts on their well‐being and sense of self.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.024 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".